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1. Berndt, D. J., & Clifford, J. (1994). Using dynamic time warping to find patterns in time series. Paper presented at the KDD workshop. 2. Cuong, N. A., Mai, D. S., Hop, D. T., Ngo, L. T., & Long, P. T. (2021). Fuzzy C-Medoids Clustering Based on Interval Type-2 Inituitionistic Fuzzy Sets. Paper presented at the 2021 RIVF International Conference on Computing and Communication Technologies (RIVF). 3. Itakura, F. (1975). Minimum prediction residual principle applied to speech recognition. IEEE Transactions on acoustics, speech, and signal processing, 23(1), 67-72. 4. Izakian, H., Pedrycz, W., & Jamal, I. (2015). Fuzzy clustering of time series data using dynamic time warping distance. Engineering Applications of Artificial Intelligence, 39, 235-244. 5. Jin, X., & Han, J. (2010). K-Medoids Clustering, Encyclopedia of Machine Learning. In: Springer US. 6. Kaufman, L., & Rousseeuw, P. J. (1990). Partitioning around medoids (program pam). Finding groups in data: an introduction to cluster analysis, 344, 68-125. 7. Keogh, E., & Ratanamahatana, C. A. (2005). Exact indexing of dynamic time warping. Knowledge and information systems, 7(3), 358-386. 8. Kim, S.-W., Park, S., & Chu, W. W. (2001). An index-based approach for similarity search supporting time warping in large sequence databases. Paper presented at the Proceedings 17th international conference on data engineering. 9. Krishnapuram, R., Joshi, A., Nasraoui, O., & Yi, L. (2001). Low-complexity fuzzy relational clustering algorithms for web mining. IEEE transactions on Fuzzy Systems, 9(4), 595-607. 10. Labroche, N. (2010). New incremental fuzzy c medoids clustering algorithms. Paper presented at the 2010 Annual Meeting of the North American Fuzzy Information Processing Society. 11. Petitjean, F., Ketterlin, A., & Gançarski, P. (2011). A global averaging method for dynamic time warping, with applications to clustering. Pattern recognition, 44(3), 678-693. 12. Ratanamahatana, C. A., & Keogh, E. (2005). Three myths about dynamic time warping data mining. Paper presented at the Proceedings of the 2005 SIAM international conference on data mining. 13. Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on acoustics, speech, and signal processing, 26(1), 43-49. 14. Sammut, C., & Webb, G. I. (2017). Encyclopedia of machine learning and data mining: Springer Publishing Company, Incorporated. 15. Torra, V. (2015). On the selection of m for Fuzzy c-Means. Paper presented at the IFSA-EUSFLAT. 16. Yi, B.-K., Jagadish, H. V., & Faloutsos, C. (1998). Efficient retrieval of similar time sequences under time warping. Paper presented at the Proceedings 14th International Conference on Data Engineering.
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